TensorFlow scales the learning system

A dataflow system supported machine-learning workloads across different computing devices.

Martín Abadi and colleagues

AI topics

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The contribution

The TensorFlow systems paper describes a framework for expressing and executing machine-learning computations across heterogeneous hardware. It addresses the engineering needed to connect experimentation with larger-scale training and deployment.

What this does not establish

A software framework enables many models; it is not itself an intelligence algorithm or evidence that any application built with it is reliable.

Why this date?

The paper was first submitted on 27 May 2016. The software was released earlier, in 2015.

This entry follows the linked publication. Read the source and date conventions.

The original work

TensorFlow: A system for large-scale machine learning

Authors: Martín Abadi and colleagues
Publication: arXiv:1605.08695; OSDI 2016
Source type: Preprint / conference paper

Paper and version history

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Cited By

Atlas entries whose reference lists cite this work or the publication version noted below.

Learning Transferable Visual Models From Natural Language Supervision (2021)
Listed in the source bibliography. Check the reference list.

Citations

Works in the Atlas cited by this entry.

Learning representations by back-propagating errors (1986)
Reference 64 gives a 1988 Cognitive Modeling citation but links to the original Nature paper. The Atlas dates the 1986 original. Check the reference list.
ImageNet Classification with Deep Convolutional Neural Networks (2012)
Listed in the source bibliography. Check the reference list.
Efficient Estimation of Word Representations in Vector Space (2013)
Listed in the source bibliography. Check the reference list.
Generative Adversarial Networks (2014)
Reference 25 cites the 2014 conference paper under its title Generative Adversarial Nets. Check the reference list.
Deep Residual Learning for Image Recognition (2015)
Listed in the source bibliography. Check the reference list.
Human-level control through deep reinforcement learning (2015)
Listed in the source bibliography. Check the reference list.

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Reference section 1 (PDF pages 13, 14, 15, 16, 17, 18)

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